{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1.2, 1.5, 1.8],\n",
    "              [1.3, 1.4, 1.9],\n",
    "             [1.1, 1.6, 1.7]])\n",
    "y = np.array([5, 10, 9]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 使用循环的方式计算每天的采购总金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[37.2, 37.599999999999994, 36.8]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_sum = []\n",
    "for x in range(len(X)):\n",
    "    _sum.append((X[x]*y).sum())\n",
    "_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 使用矩阵点乘来计算每天的采购总金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[37.2 37.6 36.8]\n"
     ]
    }
   ],
   "source": [
    "_sum1 = np.dot(X,y)\n",
    "print(_sum1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 测试两种方式的性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.29 µs ± 269 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "_sum = []\n",
    "for x in range(len(X)):\n",
    "    _sum.append((X[x]*y).sum())\n",
    "_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "980 ns ± 46.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit _sum1 = np.dot(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用numpy方法速度明显高于python循环。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "X = np.random.randint(1, 10, size=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 6, 1, 1, 2, 8, 7, 3, 5, 6, 3, 5, 3, 5, 8, 8, 2, 8, 1, 7, 8,\n",
       "       7, 2, 1, 2, 9, 9, 4, 9])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.将X处理为一个3列的矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 6],\n",
       "       [1, 1, 2],\n",
       "       [8, 7, 3],\n",
       "       [5, 6, 3],\n",
       "       [5, 3, 5],\n",
       "       [8, 8, 2],\n",
       "       [8, 1, 7],\n",
       "       [8, 7, 2],\n",
       "       [1, 2, 9],\n",
       "       [9, 4, 9]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.reshape(-1,3)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.将第三列中，小于等于3的修改为0、大于3且小于等于6的修改为1、大于6的修改为2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X[0:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[y <= 3] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[y > 6] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[y > 3] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 2, 0, 2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 1],\n",
       "       [1, 1, 0],\n",
       "       [8, 7, 0],\n",
       "       [5, 6, 0],\n",
       "       [5, 3, 1],\n",
       "       [8, 8, 0],\n",
       "       [8, 1, 2],\n",
       "       [8, 7, 0],\n",
       "       [1, 2, 2],\n",
       "       [9, 4, 2]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.分离出样本的特征和分类 标记，分别存放在两个变量中，用 X_train 存放样本特征, y_train存放分类标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X[:,[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [5, 3],\n",
       "       [8, 8],\n",
       "       [8, 1],\n",
       "       [8, 7],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = X[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 2, 0, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.用 numpy 的比较运算，通过 y_train 中的数据，分离出 X_train 中的 3 个分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [8, 8],\n",
       "       [8, 7]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分类为0的样本\n",
    "X[y_train == 0][:,[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [5, 3]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分类为1的样本\n",
    "X[y_train == 1][:,[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 1],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分类为2的样本\n",
    "X[y_train == 2][:,[0,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业三"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.将数据导入pandas中，加上列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542\\t/front-api/bill/create\\t8\\t1057.31...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644\\t/front-api/bill/create\\t5\\t749.12\\t103...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742\\t/front-api/bill/create\\t5\\t845.84\\t136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943\\t/front-api/bill/create\\t3\\t568.89\\t138...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   0\n",
       "0  2019162542\\t/front-api/bill/create\\t8\\t1057.31...\n",
       "1  162644\\t/front-api/bill/create\\t5\\t749.12\\t103...\n",
       "2  162742\\t/front-api/bill/create\\t5\\t845.84\\t136...\n",
       "3  162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...\n",
       "4  162943\\t/front-api/bill/create\\t3\\t568.89\\t138..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header = None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('log.txt', header = None, sep = '\\t',\n",
    "                 names = ['id', 'api', 'count', 'res_time_sum', 'res_time_min',\n",
    "                            'res_time_max', 'res_time_avg', 'interval', 'created_at'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('log.txt', header = None, sep = '\\t')\n",
    "#df.columns = ['id', 'api', 'count', 'res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg', 'interval', 'created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.检测是否有重复值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()  # 查看内存占用空间及数据是否存在缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179496"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.drop_duplicates()) #去重操作后与原数据大小一致，无重复数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.检测是否有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153843</th>\n",
       "      <td>11466400</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1603.03</td>\n",
       "      <td>81.63</td>\n",
       "      <td>539.04</td>\n",
       "      <td>178.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 21:50:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88713</th>\n",
       "      <td>6748463</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1321.85</td>\n",
       "      <td>75.93</td>\n",
       "      <td>392.42</td>\n",
       "      <td>165.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-12 18:26:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173406</th>\n",
       "      <td>12963962</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>287.30</td>\n",
       "      <td>77.78</td>\n",
       "      <td>121.28</td>\n",
       "      <td>95.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-24 01:32:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26286</th>\n",
       "      <td>2512270</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>587.17</td>\n",
       "      <td>96.66</td>\n",
       "      <td>184.09</td>\n",
       "      <td>146.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-01 17:16:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114637</th>\n",
       "      <td>8482643</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1249.51</td>\n",
       "      <td>73.80</td>\n",
       "      <td>482.81</td>\n",
       "      <td>178.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-17 23:06:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74149</th>\n",
       "      <td>5775907</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>1387.72</td>\n",
       "      <td>78.77</td>\n",
       "      <td>192.13</td>\n",
       "      <td>126.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-01-26 15:21:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84831</th>\n",
       "      <td>6492994</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>1093.04</td>\n",
       "      <td>171.24</td>\n",
       "      <td>384.47</td>\n",
       "      <td>273.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-07 23:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164658</th>\n",
       "      <td>12298353</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>800.21</td>\n",
       "      <td>152.28</td>\n",
       "      <td>231.33</td>\n",
       "      <td>200.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-14 11:46:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132209</th>\n",
       "      <td>9803716</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>92.88</td>\n",
       "      <td>92.88</td>\n",
       "      <td>92.88</td>\n",
       "      <td>92.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-07 12:17:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146866</th>\n",
       "      <td>10921101</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>1374.84</td>\n",
       "      <td>105.88</td>\n",
       "      <td>320.37</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-23 23:00:41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "153843  11466400  /front-api/bill/create      9       1603.03         81.63   \n",
       "88713    6748463  /front-api/bill/create      8       1321.85         75.93   \n",
       "173406  12963962  /front-api/bill/create      3        287.30         77.78   \n",
       "26286    2512270  /front-api/bill/create      4        587.17         96.66   \n",
       "114637   8482643  /front-api/bill/create      7       1249.51         73.80   \n",
       "74149    5775907  /front-api/bill/create     11       1387.72         78.77   \n",
       "84831    6492994  /front-api/bill/create      4       1093.04        171.24   \n",
       "164658  12298353  /front-api/bill/create      4        800.21        152.28   \n",
       "132209   9803716  /front-api/bill/create      1         92.88         92.88   \n",
       "146866  10921101  /front-api/bill/create      7       1374.84        105.88   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "153843        539.04         178.0        60  2019-05-01 21:50:49  \n",
       "88713         392.42         165.0        60  2019-02-12 18:26:08  \n",
       "173406        121.28          95.0        60  2019-05-24 01:32:14  \n",
       "26286         184.09         146.0        60  2018-12-01 17:16:09  \n",
       "114637        482.81         178.0        60  2019-03-17 23:06:02  \n",
       "74149         192.13         126.0        60  2019-01-26 15:21:42  \n",
       "84831         384.47         273.0        60  2019-02-07 23:00:01  \n",
       "164658        231.33         200.0        60  2019-05-14 11:46:05  \n",
       "132209         92.88          92.0        60  2019-04-07 12:17:23  \n",
       "146866        320.37         196.0        60  2019-04-23 23:00:41  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10) # 随机采样，多次执行，数据不一样，看大概"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "info中所有数据均为179496条，不存在空值、异常数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分析api和interval这两列的数据是否对分析有用，如果无用，说明为什么后将这两列丢弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe() #异常值为1，忽略不计。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "异常值为1，忽略不计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].describe() #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有值均为60，无分析意义。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['api','interval'], axis = 1) # 优化内存， 指定axis，删除“api”、“interval”列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()  #验证删除结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 使用created_at这一列的数据作为时间索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-01-12 01:14:16\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index # 当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['created_at']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 7 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(2), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 分析api调用次数情况，例如，在一天中，哪些时间是访问高峰，哪些时间段访问比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.877739e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.012494e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.626440e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825233e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811510e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981455e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.019163e+09</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.877739e+06       7.175909    1393.177832     108.419626   \n",
       "std    6.012494e+06       4.325160    1499.486073      79.640693   \n",
       "min    1.626440e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825233e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811510e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981455e+06      10.000000    1834.117500     116.990000   \n",
       "max    2.019163e+09      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  \n",
       "count  179496.000000  179496.000000  \n",
       "mean      359.880374     187.812208  \n",
       "std       638.919827     224.464813  \n",
       "min        36.550000      36.000000  \n",
       "25%       198.280000     144.000000  \n",
       "50%       256.090000     167.000000  \n",
       "75%       374.410000     202.000000  \n",
       "max    142468.270000   71325.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 切出一天的数据，绘制一天时段的接口调用情况\n",
    "plt.figure(figsize = (15, 5))\n",
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用count重采样，用一个小时进行采样，使图像平滑\n",
    "df2 = df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize = (15, 5))\n",
    "plt.figure(figsize = (15, 5))\n",
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从凌晨2点到11点访问少，下午2-3点是第一个访问高峰，晚上8-9点是第二个访问高峰"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 分析一天中api响应时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如图所示，可以看到在业务高峰时间段，最大响应时间和平均响应时间都有所上升"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 分析连续的几天数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "df['2019-5-1' : '2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如图所示，每天的业务高峰时段都比较相似。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 分析周末访问量是否有增加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekday'] = df.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \n",
       "created_at                                                                     \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07        225.73         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07        196.61         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07        232.02         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['weekend'] = df['weekday'].isin({5, 6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 周末和非周末数据叠加\n",
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如图所示，周末的下午和晚上，比非周末访问量多一些。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
